D4

Targets:

image

mask

bboxes

keypoints

Image Types:uint8, float32

Apply one of eight D4 square symmetries (rotations and reflections). Keeps square shape. Use group_element for deterministic TTA (e.g. run all 8 then inverse).

The D4 group transformations include:

'e' (identity): No transformation is applied.
'r90' (rotation by 90 degrees counterclockwise)
'r180' (rotation by 180 degrees)
'r270' (rotation by 270 degrees counterclockwise)
'v' (reflection across the vertical midline)
'hvt' (reflection across the anti-diagonal)
'h' (reflection across the horizontal midline)
't' (reflection across the main diagonal)

Even if the probability (p) of applying the transform is set to 1, the identity transformation 'e' may still occur, which means the input will remain unchanged in one out of eight cases.

When group_element is specified, the transform is deterministic—useful for TTA (Test Time Augmentation) where you need to apply each of the 8 symmetries explicitly and invert predictions. Call inverse() on a deterministic instance to get a new transform that undoes the operation.

Arguments

p

float

Probability of applying the transform. Default: 1.0.

group_element

e | r90 | r180 | r270 | v | hvt | h | t |

If set, always apply this specific D4 group element instead of sampling randomly. Use for TTA. Default: None (random choice).

Examples

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.Compose([
...     A.D4(p=1.0),
... ])
>>> transformed = transform(image=image)
>>> transformed_image = transformed['image']
# The resulting image will be one of the 8 possible D4 transformations of the input

>>> # TTA: apply each D4 symmetry, run inference, then undo the transform on the prediction
>>> from albumentations.core.type_definitions import d4_group_elements
>>> predictions = []
>>> for element in d4_group_elements:
...     aug = A.D4(p=1.0, group_element=element)
...     aug_image = aug(image=image)["image"]
...     pred_mask = np.zeros((100, 100, 1), dtype=np.uint8)  # placeholder for model output
...     restored = aug.inverse()(image=pred_mask)["image"]
...     predictions.append(restored)

Notes

This transform is particularly useful for augmenting data that does not have a clear orientation, such as top-view satellite or drone imagery, or certain types of medical images.
The input image should be square-shaped for optimal results. Non-square inputs may lead to unexpected behavior or distortions.
When applied to bounding boxes or keypoints, their coordinates will be adjusted according to the selected transformation.
This transform preserves the aspect ratio and size of the input.
inverse() requires group_element to be set explicitly; raises ValueError otherwise.

>>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> transform = A.Compose([ ... A.D4(p=1.0), ... ]) >>> transformed = transform(image=image) >>> transformed_image = transformed['image'] # The resulting image will be one of the 8 possible D4 transformations of the input >>> # TTA: apply each D4 symmetry, run inference, then undo the transform on the prediction >>> from albumentations.core.type_definitions import d4_group_elements >>> predictions = [] >>> for element in d4_group_elements: ... aug = A.D4(p=1.0, group_element=element) ... aug_image = aug(image=image)["image"] ... pred_mask = np.zeros((100, 100, 1), dtype=np.uint8) # placeholder for model output ... restored = aug.inverse()(image=pred_mask)["image"] ... predictions.append(restored)